1. Field of the Invention
The present invention relates a method and system using social media for real-time event driven trading of equities, commodities and other traded assets.
2. Description of the Related Art
Sentiment analysis applies various analytical techniques in identifying subjective information from different information sources. Sentiment analysis, therefore, attempts to ascertain the feelings, thoughts, attitude, opinion, etc. of a speaker or a writer with respect to a topic.
Most work on sentiment analysis has relied on two main approaches. The first approach, in particular, a so called “bag of words” approach, attempts to apply a positive/negative document classifier based on occurrence frequencies of the various words in a document. Applying this approach various learning methods can be used to select or weight different parts of the text used in the classification process. This approach fails to process the sentiment with respect to assets (for example, equities or commodities) in short digital messages such as tweets sent via the online social networking service Twitter.
The second approach is “semantic orientation.” Semantic orientation automatically classifies words into two classes, “good” and “bad”, and then computes an overall good/bad score for the text. This method does not take into consideration the sentiment conveyed by parts of speech other than adjectives, including verbs, for example, to bounce, to crash, nouns, for example, a put, a call, and phrases, for example, ascending triangle, black Friday, head-and-shoulders.
Both methods fail to determine the sentiment with respect to specific assets in short digital messages such as tweets sent via the online social networking service Twitter. Their main pitfall is that they fail to process the sentiment in the syntax-semantic context of the message.